Zero resource speech processing problem is generally addressed by acoustic unit discovery (AUD). Since the phonetic transcriptions are not available for the audio, AUD aims to discover a set of acoustic units directly from the audio. The obtained transcriptions, either as tokens or as posterior representation, are used to train models. As only audio is available for training, the problem is an unsupervised clustering problem, also termed as segmentation problem. Clustering similar feature vectors to form speech units is the most common approach [1]}, [2]}, [3]} . The final acoustic unit models are obtained by modelling the clusters. One of the principal issues in AUD is to identify the initial time scale for processing speech. In most existing approaches, a frame of about 25ms length is chosen as the smallest unit for clustering. The frames are clustered based on a similarity measure such as Euclidean distance; For segmentation problem, nearby frames with high similarity measure are merged based on a bottom-up hierarchical clustering approach [1]}, [3]}. Bayesian approaches can be either parametric or non-parametric. Parametric approaches [2]}, [3]} for AUD use expectation-maximization approach to cluster or discover and model AUs. Non-parametric approaches use variational inference [8]} or Gibbs sampling [9]}, [10]}, [11]}.
The approaches can be probabilistic generative modelling followed by ANN based discriminative clustering [12]}, [13]}. There have been a few top-down approaches proposed in the literature[14]}, [15]}. In [14]}, the states in an HMM are iteratively split into multiple states based on a splitting criterion. In [15]}, the mixtures of UBM are clustered into a set of AUs.
